Modeling queue-based message-oriented middleware relationships in a security system

ABSTRACT

A system and a method for modeling queue-based message-oriented middleware (MoM) are provided. The method commences with connecting with a MoM system and converting information associated with the MoM system into a standardized object model. The standardized object model may include a queue-based node, at least one producer application, and at least one consumer application. The at least one producer application provides a message to the queue-based node. The at least one consumer application receives the message from the queue-based node. The message persists in the queue until consumed by the at least one consumer application. The method continues with generating a standardized graph of relationships between a producer and a consumer over a period of time. The method further includes creating a policy, periodically analyzing the standardized graph for at least one deviation from the policy, and issuing an alert in response to detecting the at least one deviation.

TECHNICAL FIELD

The present disclosure relates generally to data processing and, moreparticularly, to systems and methods for modeling queue-basedmessage-oriented middleware (MoM) within security and operationalmanagement systems.

BACKGROUND

Currently, there exist many different systems providing MoM withinenterprise solutions. However, the existing MoM systems are notstandardized and, therefore, are delivered and instrumented usingvarying approaches. The lack of standardization makes it difficult tomonitor and ensure that proper security controls are in place whendealing with multiple MoM systems. It also makes operational andbusiness continuity planning challenging as understanding end-to-endapplication dependencies through MoM systems is inconsistent andcomplex. Because the existing MoM systems are complex and theirinterfaces are not standardized, enterprise security teams do notreadily have access to information. There is currently no reliable wayof interfacing with different systems and making it simple andaccessible for a security team or application developers to understandall of their use of MoM to facilitate security of these systems andunderstanding of application dependencies for operational purposes.These difficulties exist in private data centers, private and publiccomputing clouds, and various combinations thereof.

Because all of the existing solutions are deployed above a networkinglayer, normal network monitoring tools and normal network securitytools, such as security groups, virtual private cloud flow logs,monitoring telemetry, and so forth cannot be used. There is currently nointerface or integration between all of these different systems, as wellas no integration into a standardized model that would make it simplefor an application developer to create data flow diagrams, such thatthey can review the usage, identify when the usage changes, and getalerted. Some existing systems operate on an open basis by default,whereas others do not. For open systems, a rogue actor could potentiallyjoin a queue and gain access to all of the messaging because all of thetransport layer encryption is below the system. Thus, there is currentlyno system that would allow providing consistent sets of permissions tominimize the amount of access to ensure that only a specific system withaccess to a given queue gets the access.

Moreover, there are regulatory requirements for critical businessfunctions to be reviewed on a periodic basis. Conventionally, data to bereviewed are generated manually or developers of the system makeassumptions as to what the data mean and on the relationships betweencomponents of the system.

SUMMARY

This summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter.

Provided are systems and methods for modeling queue-based MoM. Accordingto one example embodiment, an enterprise security system for modelingqueue-based MoM may include a cloud connector, a graphical unit, and apolicy compute engine. The cloud connector may be configured to connectwith a MoM system and convert information associated with the MoM systeminto a standardized object model. The standardized object model mayinclude a queue-based node associated with a queue of the MoM system, atleast one producer application, and at least one consumer application.The at least one producer application may be configured to provide amessage to the queue-based node. The message may be generated by aproducer associated with the at least one producer application. The atleast one consumer application may be configured to receive the messagefrom the queue-based node. The message may be consumed by a consumerassociated with the at least one consumer application. The message maypersist in the queue until consumed by the at least one consumerapplication. The graphical unit may be configured to generate astandardized graph of relationships between the producer and theconsumer over a period of time. The standardized graph can be viewed orexported in a human readable report (e.g., a Portable Document Format(PDF) report) or a machine-readable report (e.g., a Comma-SeparatedValues (CSV) report or a JavaScript Object Notation (JSON) report). Thepolicy compute engine may be configured to create a policy andperiodically analyze the standardized graph for at least one deviationfrom the policy. In response to detecting the at least one deviation,the policy compute engine may issue an alert.

According to another example embodiment, a method for modelingqueue-based MoM is provided. The method may commence with connecting, bya cloud connector, with a MoM system. The method may include converting,by the cloud connector, information associated with the MoM system intoa standardized object model. The standardized object model may include aqueue-based node associated with a queue of the MoM system, at least oneproducer application, and at least one consumer application. The atleast one producer application may provide a message to the queue-basednode. The message may be generated by a producer associated with the atleast one producer application. The at least one consumer applicationmay receive the message from the queue-based node. The message may beconsumed by a consumer associated with the at least one consumerapplication. The message may persist in the queue until consumed by theat least one consumer application. The method may continue withgenerating, by a graphical unit, a standardized graph of relationshipsbetween the producer and the consumer over a period of time. The methodmay further include creating a policy by a policy compute engine. Thepolicy compute engine may periodically analyze the standardized graphfor at least one deviation from the policy. The method may furtherinclude issuing an alert by the policy compute engine in response todetecting the at least one deviation.

Additional objects, advantages, and novel features will be set forth inpart in the detailed description section of this disclosure, whichfollows, and in part will become apparent to those skilled in the artupon examination of this specification and the accompanying drawings ormay be learned by production or operation of the example embodiments.The objects and advantages of the concepts may be realized and attainedby means of the methodologies, instrumentalities, and combinationsparticularly pointed out in the appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments are illustrated by way of example and not limitation in thefigures of the accompanying drawings, in which like references indicatesimilar elements.

FIG. 1 is a block diagram of an environment, in which systems andmethods for modeling queue-based message-oriented middleware can beimplemented, according to some example embodiments.

FIG. 2 shows an enterprise security system for modeling queue-basedmessage-oriented middleware, according to an example embodiment.

FIG. 3 is a flow chart of an example method for modeling queue-basedmessage-oriented middleware, according to some example embodiments.

FIG. 4 shows a controller implemented in an enterprise security systemfor modeling queue-based message-oriented middleware, according to anexample embodiment.

FIG. 5 shows a computing system that can be used to implement a systemand a method for modeling queue-based message-oriented middleware,according to an example embodiment.

DETAILED DESCRIPTION

The following detailed description includes references to theaccompanying drawings, which form a part of the detailed description.The drawings show illustrations in accordance with example embodiments.These example embodiments, which are also referred to herein as“examples,” are described in enough detail to enable those skilled inthe art to practice the present subject matter. The embodiments can becombined, other embodiments can be utilized, or structural, logical, andelectrical changes can be made without departing from the scope of whatis claimed. The following detailed description is, therefore, not to betaken in a limiting sense, and the scope is defined by the appendedclaims and their equivalents.

The present disclosure provides methods and systems for modeling queuebased message-oriented middleware. The proposed systems provide a way ofinterfacing with different applications/platforms that communicate witheach other and facilitating access for a security team or someapplication developers to better understand their use of a queue-basedmessage-oriented middleware to simplify providing security for thoseapplications/platforms and to understand application dependencies.

The systems of the present disclosure can interface with differentapplications, integrate information into a standardized model, andfacilitate creation of data flow diagrams such that any unexpected usagechanges can be detected, and alerts issued. Furthermore, the systems ofthe present disclosure allow creating sets of permissions to minimizethe amount of access, thereby ensuring that only certain systems areprovided with access to a message queue.

The systems and methods disclosed herein can use an enterprise securitysystem defined as the application controller (also referred herein to asa controller) to create a standardized model in a graph in order todescribe how queue-based message-oriented middleware work.

Referring now to the drawings, FIG. 1 is a block diagram of environment100, in which methods for modeling queue-based message-orientedmiddleware can be implemented, according to some example embodiments.The environment 100 may include an enterprise network 105 and anenterprise security system 200 (referred herein to as a system 200) formodeling queue-based message-oriented middleware. The enterprise network105 may be associated with an enterprise (not shown) and may have aplurality of distributed systems shown as application servers 110, 115,and 120, which may communicate with each other. The distributed systemsmay include applications, Enterprise JavaBeans, servlets, and othercomponents and may run on different networked platforms. A MoM system125 may be provided in the enterprise network 105 to support theinteractions between the application servers 110, 115, and 120 byproviding a Publish/Subscribe communications channel known commonly asan Enterprise Message Bus.

The MoM system 125 is software or hardware infrastructure that supportssending and receiving messages between distributed systems. Dataproducers (also called publishers) may not send data directly to adestination service but may put the data onto a distributed broker or anEnterprise Message Bus (such as the MoM system 125) where the data waituntil data consumers retrieve the data. Therefore, application modules,such as application servers 110, 115, and 120, may be distributed overheterogeneous platforms and may communicate with each other via the MOMsystem 125. For example, the application server 110 may communicate withthe application server 115 and the application server 115 maycommunicate with the application server 120 via the MOM system 125.Specifically, the MOM system 125 may act as a message broker between theapplication servers 110, 115, and 120 and may receive messages from oneserver and send the messages to and/or store the messages for otherservers. For example, the MOM system 125 may serve communications 130sent between the application server 110 and the application server 115and communications 135 sent between the application server 115 and theapplication server 120.

The MOM system 125 creates a distributed communications layer thatinsulates an application developer from information related to variousoperating systems and network interfaces associated with the applicationservers 110, 115, and 120. The MOM system 125 may have an API that canbe used by the application servers 110, 115, and 120 distributed ondifferent network nodes to communicate. Software elements of the MOMsystem 125 may reside in all communicating components of the enterprisenetwork 105 and may typically support asynchronous calls between clientand server applications.

As can be seen in FIG. 1 , from a networking perspective, theapplication server 110 communicates with the MOM system 125 and then theMOM system 125 communicates with the application server 115; and theapplication server 115 communicates with the MOM system 125 and then theMOM system 125 communicates with the application server 120. Meanwhile,from a perspective of relationships between the application servers, theapplication server 110 is actually communicating with the applicationserver 115 by sending communications 130, and the application server 115is actually communicating with the application server 120 by sendingcommunications 135.

The communications may be produced (or published) by one applicationserver (acting as producer of messages) and consumed (or read) by otherapplication servers (acting as consumers of messages). In an exampleembodiment, the MoM system 125 may use a queue-based approach ofcommunicating messages between the application servers 110, 115, and120. The MoM system 125 may create a queue 155 for storing messagesreceived from the application servers. Specifically, a message produced(or published) by one application server (acting as producers of themessage) may be put into the queue 155 and stored in the queue 155 untilthe message is consumed (or read) by another application server (actingas a consumer of the message). After the consumer reads the messagestored in the queue 155, the message may be deleted from the queue 155.Therefore, in the queue-based approach, the message is stored in a queuein the queue 155 until a consumer reads the message from the queue. Thisqueue-based messaging approach can be used for critical and sensitivedata.

To provide a full end-to-end application visibility of the enterprisenetwork 105, the enterprise security system 200 for modeling queue-basedmessage-oriented middleware is provided and connected to the MoM system125. In an example embodiment, the system 200 may communicate with theMoM system 125 via a data network 140.

The data network 140 may include the Internet or any other networkcapable of communicating data between devices. Suitable networks mayinclude or interface with any one or more of, for instance, a localintranet, a corporate data network, a data center network, a home datanetwork, a Personal Area Network, a Local Area Network (LAN), a WideArea Network (WAN), a Metropolitan Area Network, a virtual privatenetwork, a storage area network, a frame relay connection, an AdvancedIntelligent Network connection, a synchronous optical networkconnection, a digital T1, T3, E1 or E3 line, Digital Data Serviceconnection, Digital Subscriber Line connection, an Ethernet connection,an Integrated Services Digital Network line, a dial-up port such as aV.90, V.34 or V.34bis analog modem connection, a cable modem, anAsynchronous Transfer Mode connection, or a Fiber Distributed DataInterface or Copper Distributed Data Interface connection. Furthermore,communications may also include links to any of a variety of wirelessnetworks, including Wireless Application Protocol, General Packet RadioService, Global System for Mobile Communication, Code Division MultipleAccess or Time Division Multiple Access, cellular phone networks, GlobalPositioning System, cellular digital packet data, Research in Motion,Limited duplex paging network, Bluetooth radio, or an IEEE 802.11-basedradio frequency network. The data network 140 can further include orinterface with any one or more of a Recommended Standard 232 (RS-232)serial connection, an IEEE-1394 (FireWire) connection, a Fiber Channelconnection, an IrDA (infrared) port, a Small Computer Systems Interfaceconnection, a Universal Serial Bus (USB) connection or other wired orwireless, digital or analog interface or connection, mesh or Digi®networking.

The system 200 may receive information 145 associated with the MoMsystem 125, process the collected information 145, and generate astandardized graph 150 of relationships between the distributed systems,such as application servers 110, 115, and 120 over a period of time. Thestandardized graph 150 can be viewed or exported in a human readablereport (e.g., a PDF report) or a machine-readable report (e.g., a CSVreport or a JSON report).

FIG. 2 shows an enterprise security system 200 for modeling queue-basedmessage-oriented middleware. In an example embodiment, the system 200may act as an application controller shown as a controller 400. Thesystem 200 may include a cloud connector 205, a graphical unit 210, anda policy compute engine 215. The cloud connector 205 may be configuredto connect with a MoM system, such as the MoM system 125 shown in FIG. 1. The MoM system may be associated with a private data center, a privatecloud, a public cloud, and so forth. Example MoM systems include ApacheKafka, IBM MQ, RabbitMQ, TIBCO Rendezvous, AWS Kinesis, Microsoft AzureService Bus, Microsoft Message Queuing (MSMQ), NServiceBus, Simple QueueService (SQS), Simple Notification Service (SNS), Advanced MessageProcessing System (AMPS), and so forth.

The cloud connector 205 may be configured to receive informationassociated with the MoM system and convert the received information intoa standardized object model 220. In an example embodiment, theinformation received from the MoM system by the cloud connector 205 mayinclude a telemetry feed with events of the MoM system (e.g., IBM MQaccounting). In this embodiment, the cloud connector 205 may consume astream of the telemetry feed (e.g., process and directional metadata),convert the events into relationships, and summarize the relationships.If, for example, there are thousands of producer applications 230producing messages to a queue associated with the queue-based node 225and one of consumer applications 235 is consuming these messages, thecloud connector 205 may summarize the information related to messages toonly one entry stating that the producer applications 230 produced Nmessages (where N is the number of messages) to the queue consumed byone of the consumer applications 235. Therefore, the informationassociated with the MoM system received by the cloud connector 205 maybe compressed when converting the information into the standardizedobject model 220.

In a further example embodiment, the MoM system may not support thetelemetry streaming (e.g., Kafka). In this embodiment, the informationreceived from the MoM system by the cloud connector 205 may include adata snapshot of requests from the MoM system. The cloud connector 205may be configured to periodically request the data snapshot of requestsfrom the MoM system or receive the data snapshot directly from the MoMsystem through logs and/or accounting information, determine statusinformation based on the data snapshot, and convert the statusinformation into the standardized object model.

The standardized object model 220 may include a node associated with theMoM system 125 and a queue-based node 225 associated with the MoMsystem, one or more producer applications 230, and one or more consumerapplications 235. The queue-based node 225 may be associated with aqueue created by the MoM system for messages and may have a queue namerelating to the queue. The MoM system may enable writing one message ata time to the queue by one of the producers and reading differentmessages from the queue by multiple consumers. The producerapplication(s) 230 may be configured to provide a message to thequeue-based node 225. The message may be generated by a producerassociated with the producer application(s) 230. The consumerapplication 235 may be configured to receive the message from thequeue-based node 225. The message may be consumed by a consumerassociated with the consumer application 235. If there are no consumerapplications available, the message may be stored in the queue until aconsumer application that reads the message appears. The message maypersist in the queue until consumed by the consumer application. In someembodiments, the producer application(s) 230 may be configured toreceive a confirmation once one of the consumer applications 235receives the message. Upon reading the message by one of the consumerapplications 235, the message may be deleted from the queue so that noother consumer applications can read the message. Therefore, only oneconsumer application can read the message from the queue. Multipleconsumer applications can read messages in a round robin fashion, butone message goes to only one consumer application. This queue-basedmessaging approach can be used for critical and sensitive data.

The graphical unit 210 may use the standardized object model 220 togenerate a standardized graph of relationships 240 between the one ormore producers and the one or more consumers over a period of time. Thestandardized graph may visualize the information collected by the cloudconnector 205 for network operators to facilitate understanding ofconfiguration types and specific parameters of a data network.

For example, the standardized graph may show relationships between anapplication sending messages and an application receiving the messages.The standardized graph may also consolidate communications related tothe queue across the MoM system 125, Transmission ControlProtocol/Internet Protocol (TCP/IP) communications, and othercommunications.

When the system 200 puts the information into the standardized graph,the system 200 can record one instance of each relationship at a timeover a period of time (e.g., months) for the producer applications 230producing messages for the queue. Thus, the standardized graph may showwhich producer application 230 produced massages to the queue, when afirst message was produced by the producer application 230, when a lastmessage was produced by the producer application 230, the number ofmessages the producer application 230 produced, the volume of messagesthe producer application 230 produced, and so forth.

The policy compute engine 215 may be configured to create a policy. Thepolicy may be created by the policy compute engine 215 based on the oneor more producers associated with the at least one producer application,the one or more consumers associated with the at least one consumerapplication, and, optionally, the queue. For example, the policy may seta normal behavior of a system. The policy compute engine 215 mayperiodically analyze the standardized graph for at least one deviationfrom the policy. The policy compute engine 215 may issue an alert upondetecting the at least one deviation from the policy. For example, thepolicy compute engine 215 may compare new behavior of producers andconsumers against the policy and create an event if a deviation from thepolicy is detected.

In an example embodiment, the at least one deviation may be indicativeof an operational risk or a cyber risk. The at least one deviation maybe determined based on at least one of the following events: anunexpected change in usage, appearance of a new node, an unexpectedchange in a node, a new relationship between nodes (e.g., when insteadof consumer applications 1 and 2, consumer application 3 appears andstarts consuming messages generated by a producer application), athreshold of communications exceeded, events occurring at a differenttime, a higher Service Level Agreement (SLA) requirement, a RecoveryTime Objective (RTO) mismatch between nodes, a Recovery Point Objective(RPO) mismatch between nodes, and so forth.

The analysis of the standardized graph may be performed based on one ormore of the following: baselined historical data, a set of predeterminedpermissions, regulatory requirements, and so forth. For example, theevents that deviate from the policy may include exceeding threshold ofcommunication volume, events happening at different time of day thanexpected, appearance of a new producer application or a new consumerapplication, and so forth.

In an example embodiment, the events that deviate from the policy mayinclude an event when a new producer application or consumer applicationbecomes dependent on the queue (which means joining the queue by a newmember and indicating that the new member is producing or retrievingdata from an enterprise system). In this embodiment, a business impactanalysis may need to be performed. The standardized graph can includethe SLA, RTOs and RPOs, and operational information associated withconsumer applications and producer applications. A new consumerapplication can have a higher SLA requirement than the SLA associatedwith the producer application.

The RTO for critical functions can be asserted across complex businessinterdependencies to meet regulatory obligations. The RTO is an amountof time it takes for an application to go from a down state to an activestate. The RPO is a period of time in which an enterprise system must berestored after a disruptive event. The RTOs and RPOs can be used inoperational risk assessments to determine whether a producer application1 is appropriate to provide service to a consumer application 1. Forexample, the producer application 1 can have an RTO of 4 and theconsumer application 1 can have an RTO of 1, which means that theproducer application 1 is inappropriate to provide service to theconsumer application 1. Therefore, dynamic, continuous, transparent, andconsistent visibility of applications and dependencies and relationshipsacross the MoM system can be used to reduce the operational risk.Responding immediately to changes in interdependencies of criticalfunctions and their recovery time capabilities can be used to improvecustomer experience.

Based on the analysis of the standardized graph, the MoM system can bemonitored for behavior anomalies and a pattern of accessing the queue byapplications can be determined. Based on the standardized graph, abehavioral model of a usual behavior of applications can be built. Thedeviation from the behavioral model may result in generation of thealert. For example, the policy can be written to issue the alert if theRTO of the consumer application 1 is higher than the RTO of the producerapplication 1 to detect that there is an operational risk. The policycompute engine 215 may be further configured to create a report showingrelationships between the producer application and the consumerapplication of the MoM system.

In some embodiments, a clustering machine learning module may be builtbased on the standardized graph to monitor the MoM system for behavioranomalies.

The policy compute engine 215 may use the standardized graph to generatedata flow diagrams and infrastructure dependencies associated with theMoM system. Data flow diagrams can show relationships between producerapplications and consumer applications. Using the data flow diagrams, adata flow diagram report can be produced for the whole enterprisesystem. The data flow diagram report can be used to satisfy regulatoryrequirements for critical business functions to be reviewed on aperiodic basis. In view of this, the policy compute engine 215 may befurther configured to periodically review critical business functionsvisualized in the standardized graph.

The policy compute engine 215 may be further configured to setpermissions to control access to the queue. Based on the permissions,the MoM system can determine which producer application is allowed toproduce (publish) message to a queue and which consumer application isallowed to consume (subscribe on) messages from the queue based onprocesses running on machines associated with the producer applicationand the consumer application. In some embodiments, the determination ofwhether the application is allowed to access is performed based oncertificates, public key infrastructure, a username, a password, and soforth. The permissions allow preventing an authorized producerapplication from spoofing data and preventing an authorized consumerfrom illegitimately accessing a queue and accessing the data.

The policy compute engine 215 may be further configured to periodicallyreview critical business functions visualized in the standardized graph.The policy compute engine 215 may be further configured to take amitigating action in response to the alert. The policy compute engine215 may create a report showing relationships between the producerapplication and the consumer application of the MoM system.

FIG. 3 is a flow chart of example method 300 for modeling queue-basedmessage-oriented middleware, according to some example embodiments. Themethod 300 may commence with connecting, by a cloud connector, with aMoM system at operation 302. The method 300 may include converting, bythe cloud connector, information associated with the MoM system into astandardized object model at operation 304. The cloud connector mayreceive the information from the MoM system. The standardized objectmodel may include a queue-based node associated with the MoM system, atleast one producer application, and at least one consumer application.The producer application may provide a message to the queue-based node.The message may be generated by a producer associated with the producerapplication. The consumer application may receive the message from thequeue-based node. The message may be consumed by a consumer associatedwith the consumer application. The message may persist in the queueuntil consumed by the consumer application.

The method 300 may further include generating, by a graphical unit, astandardized graph of relationships between the producer and theconsumer over a period of time at operation 306. The method 300 maycontinue with creating a policy by a policy compute engine at operation308. At operation 310, the policy compute engine may periodicallyanalyze the standardized graph for at least one deviation from thepolicy. At operation 312, the policy compute engine may issue an alertwhen the at least one deviation is detected. The method 300 may furtherinclude creating, by the policy compute engine, a report showingrelationships between the producer application and the consumerapplication of the MoM system.

The method 300 may further include generating, via the standardizedgraph, data flow diagrams and infrastructure dependencies associatedwith the MoM system. The method 300 may further include setting, by thepolicy compute engine, permissions to control access to the queue.

FIG. 4 shows a controller 400, according to an example embodiment. Insome embodiments, the controller 400 may be implemented in the form ofan enterprise security system 200 for modeling queue-basedmessage-oriented middleware shown in FIG. 2 .

The controller 400 can receive streaming telemetry 475 from network logs470, events 485 from cloud control plane 480, and inventory 495 fromconfiguration management database (CMDB) 490.

Network logs 470 can be data sources such as flow logs from cloudservices 4601-460Z (e.g., Amazon Web Services (AWS), Microsoft Azure,and Google Cloud Platform (GCP)), vArmour DSS Distributed SecuritySystem, Software Defined Networking (SDN) (e.g., VMware NSX and CiscoApplication Centric Infrastructure (ACI)), monitoring agents (e.g.,Tanium Asset and Falco), and the like. Generally, streaming telemetry475 can be low-level data about relationships between applications.Streaming telemetry 475 can include 5-tuple, layer 7 (application layer)process information, management plane logs, and the like. 5-tuple refersto a set of five different values that comprise a TCP/IP connection: asource IP address/port number, destination IP address/port number andthe protocol in use. Streaming telemetry 475 can alternatively oradditionally include a volume of data (i.e., how much data there is orhow many data packets there are) exchanged between workloads (e.g., aphysical computing system, a virtual machine, a container, andcombinations thereof) in a network, dates and times at whichcommunications (e.g., data packets) are exchanged between workloads, andthe like.

Cloud control plane 480 establishes and controls the network andcomputing resources within a cloud computing environment (e.g.,environment 100 in FIG. 1 ). Cloud control plane 480 can includeinterfaces for managing assets (e.g., launching virtual machines and/orcontainers, configuring the network, etc.) in a cloud computingenvironment. For example, cloud control plane 480 can include one ormore instances of container orchestration, such as Docker Swarm®,Kubernetes®, Amazon EC2 Container Service (ECS), Diego, and Apache®Mesos™. By way of further non-limiting examples, cloud control plane 480can include VMware vSphere, application programming interfaces (APIs)provided by cloud services 4601-460Z, and the like.

Events 485 can include information about a container being created,having a state change, having an error, and the like. For example, whena container is created, information about the workload such as a servicename, image deployed, and the like can be received in events 485. By wayof further example, additional information from an image registrycorresponding to the deployed image can be gathered by controller 400.

The CMDB 490 can be a database of information about the hardware andsoftware components (also known as assets) used in a cloud computingenvironment (e.g., environment 100 in FIG. 1 ) and relationships betweenthose components and business functions. CMDB 490 can includeinformation about upstream sources or dependencies of components, andthe downstream targets of components. For example, inventory 495 can beused to associate an application name and other information (e.g.,regulatory requirements, business unit ownership, business criticality,and the like) with the workload it is running on.

Streaming telemetry 475, events 485, and inventory 495 can be ingestedby graph 420. Graph 420 normalizes information received in streamingtelemetry 475, events 485, and inventory 495 into a standard data formatand/or model stored in a graph database 425. Graph database 425 uses agraph data model comprised of nodes (also referred to as vertices),which is an entity such as a workload, and edges, which represent therelationship between two nodes. Edges can be referred to asrelationships. An edge can have a start node, end node, type, anddirection, and an edge can describe parent-child relationships, actions,ownership, and the like. In contrast to relational databases,relationships are (most) important in graph database 425. In otherwords, connected data is equally (or more) important than individualdata points.

Conventionally, security management systems store raw logs of each andevery individual communication between workloads. The amount of data isscaled linearly and consumes massive amounts of storage. In contrast,streaming telemetry 475, events 485, and inventory 495 can be used bygraph 420 to create and update graph database 425. The individualcommunications may be not stored. In this way, graph database 425 isadvantageously scalable. For example, graph database 425 for a largecloud computing environments of 30,000-50,000 workloads can be stored inmemory of a workload.

A graphical unit 210 can visually present information from graphdatabase 425 to users according to various criteria, such as byapplication, application type, organization, and the like. The graphicalunit 210 can visually organize information from graph database 425. Insome embodiments, nodes that behave similarly can be clustered together(i.e., be put in a cluster). For example, when two nodes have similaredges (relationships) and behave in a similar fashion (e.g., run thesame application, are associated with the same organization, and thelike), the two nodes can be clustered together. Nodes that are clusteredtogether can be visually presented as a shape (e.g., circle, rectangle,and the like), which denotes that there are a certain number ofworkloads fulfilling the same function, instead of presenting a shapefor each workload in the cluster.

The policy compute engine 215 can use information in the graph database425 to design security policies, also referred to herein as policies.The policy compute engine 215 can produce multiple security policies,each reflecting independent pieces of security logic that can beimplemented by the policy compute engine 215. Security policies canimplement security controls, for example, to protect an applicationwherever it is in a cloud computing environment (e.g., environment 100in FIG. 1 ). A security policy can specify what is to be protected(“nouns”), for example, applications run for a particular organization.A security policy can further specify a security intent (“verbs”), thatis, how to protect. For example, a security intent can be to implementPayment Card Industry Data Security Standard (PCI DSS) networksegmentation requirements (a regulatory requirement), implement securitybest practices for databases, implement a whitelist architecture, andthe like. By way of further example, a security intent can be specifiedin a template by a user (responsible for system administration,security, and the like).

Cloud drivers 4501-450Z can serve as an interface between the policycompute engine 215 (having a centralized security policy) and cloudservices 4601-460Z. In other words, cloud drivers 4501-450Z implementthe security policy using different facilities (e.g., APIs) andcapabilities available from cloud services 4601-460Z. In an exampleembodiment, the cloud drivers 4501-450Z may act as a cloud connector 205shown in FIG. 2 .

FIG. 5 illustrates an exemplary computing system 500 that may be used toimplement embodiments described herein. The computing system 500 can beimplemented in the contexts of the system 200 and the applicationcontroller 400. The exemplary computing system 500 of FIG. 5 may includeone or more processors 510 and memory 520. Memory 520 may store, inpart, instructions and data for execution by the one or more processors510. Memory 520 can store the executable code when the exemplarycomputing system 500 is in operation. The exemplary computing system 500of FIG. 5 may further include a mass storage 530, portable storage 540,one or more output devices 550, one or more input devices 560, a networkinterface 570, and one or more peripheral devices 580.

The components shown in FIG. 5 are depicted as being connected via asingle bus 590. The components may be connected through one or more datatransport means. The one or more processors 510 and memory 520 may beconnected via a local microprocessor bus, and the mass storage 530, oneor more peripheral devices 580, portable storage 540, and networkinterface 570 may be connected via one or more input/output buses.

Mass storage 530, which may be implemented with a magnetic disk drive oran optical disk drive, is a non-volatile storage device for storing dataand instructions for use by a magnetic disk or an optical disk drive,which in turn may be used by one or more processors 510. Mass storage530 can store the system software for implementing embodiments describedherein for purposes of loading that software into memory 520.

Portable storage 540 may operate in conjunction with a portablenon-volatile storage medium, such as a compact disk (CD) or digitalvideo disc (DVD), to input and output data and code to and from thecomputing system 500 of FIG. 5 . The system software for implementingembodiments described herein may be stored on such a portable medium andinput to the computing system 500 via the portable storage 540.

One or more input devices 560 provide a portion of a user interface. Theone or more input devices 560 may include an alphanumeric keypad, suchas a keyboard, for inputting alphanumeric and other information, or apointing device, such as a mouse, a trackball, a stylus, or cursordirection keys. Additionally, the computing system 500 as shown in FIG.5 includes one or more output devices 550. Suitable one or more outputdevices 550 include speakers, printers, network interfaces, andmonitors.

Network interface 570 can be utilized to communicate with externaldevices, external computing devices, servers, and networked systems viaone or more communications networks such as one or more wired, wireless,or optical networks including, for example, the Internet, intranet, LAN,WAN, cellular phone networks (e.g., Global System for Mobilecommunications network, packet switching communications network, circuitswitching communications network), Bluetooth radio, and an IEEE802.11-based radio frequency network, among others. Network interface570 may be a network interface card, such as an Ethernet card, opticaltransceiver, radio frequency transceiver, or any other type of devicethat can send and receive information. Other examples of such networkinterfaces may include Bluetooth®, 3G, 4G, and WiFi® radios in mobilecomputing devices as well as a USB.

One or more peripheral devices 580 may include any type of computersupport device to add additional functionality to the computing system.The one or more peripheral devices 580 may include a modem or a router.

The components contained in the exemplary computing system 500 of FIG. 5are those typically found in computing systems that may be suitable foruse with embodiments described herein and are intended to represent abroad category of such computer components that are well known in theart. Thus, the exemplary computing system 500 of FIG. 5 can be apersonal computer, handheld computing device, telephone, mobilecomputing device, workstation, server, minicomputer, mainframe computer,or any other computing device. The computer can also include differentbus configurations, networked platforms, multi-processor platforms, andso forth. Various operating systems (OS) can be used including UNIX,Linux, Windows, Macintosh OS, Palm OS, and other suitable operatingsystems.

Some of the above-described functions may be composed of instructionsthat are stored on storage media (e.g., computer-readable medium). Theinstructions may be retrieved and executed by the processor. Someexamples of storage media are memory devices, tapes, disks, and thelike. The instructions are operational when executed by the processor todirect the processor to operate in accord with the example embodiments.Those skilled in the art are familiar with instructions, processor(s),and storage media.

It is noteworthy that any hardware platform suitable for performing theprocessing described herein is suitable for use with the exampleembodiments. The terms “computer-readable storage medium” and“computer-readable storage media” as used herein refer to any medium ormedia that participate in providing instructions to a central processingunit (CPU) for execution. Such media can take many forms, including, butnot limited to, non-volatile media, volatile media, and transmissionmedia. Non-volatile media include, for example, optical or magneticdisks, such as a fixed disk. Volatile media include dynamic memory, suchas RAM. Transmission media include coaxial cables, copper wire, andfiber optics, among others, including the wires that include oneembodiment of a bus. Transmission media can also take the form ofacoustic or light waves, such as those generated during radio frequencyand infrared data communications. Common forms of computer-readablemedia include, for example, a floppy disk, a flexible disk, a hard disk,magnetic tape, any other magnetic medium, a CD-read-only memory (ROM)disk, DVD, any other optical medium, any other physical medium withpatterns of marks or holes, a RAM, a PROM, an EPROM, an EEPROM, aFLASHEPROM, any other memory chip or cartridge, a carrier wave, or anyother medium from which a computer can read.

Various forms of computer-readable media may be involved in carrying oneor more sequences of one or more instructions to a CPU for execution. Abus carries the data to system RAM, from which a CPU retrieves andexecutes the instructions. The instructions received by system RAM canoptionally be stored on a fixed disk either before or after execution bya CPU.

Thus, systems and methods for modeling queue-based message-orientedmiddleware are described. Although embodiments have been described withreference to specific exemplary embodiments, it will be evident thatvarious modifications and changes can be made to these exemplaryembodiments without departing from the broader spirit and scope of thepresent application. Accordingly, the specification and drawings are tobe regarded in an illustrative rather than a restrictive sense.

What is claimed is:
 1. An enterprise security system for modeling queue-based message-oriented middleware (MoM), the system comprising: a cloud connector configured to: connect with a MoM system; receive information associated with the MoM system; compress the received information associated with the MoM system; and convert the compressed information associated with the MoM system into a standardized object model; wherein the standardized object model includes: a queue-based node associated with the MoM system; at least one producer application configured to provide a message to the queue-based node, the message being generated by a producer associated with the at least one producer application; and at least one consumer application configured to receive the message from the queue-based node, the message being consumed by a consumer associated with the at least one consumer application, the message persisting in a queue until consumed by the at least one consumer application; a graphical unit configured to generate a standardized graph of relationships between a policy compute engine configured to: the producer and the consumer over a period of time; and create a policy; periodically analyze the standardized graph for at least one deviation from the policy; and in response to detecting the at least one deviation, issue an alert.
 2. The system of claim 1, wherein the at least one producer application is configured to receive a confirmation once the at least one consumer application receives the message.
 3. The system of claim 1, wherein the standardized graph is configured to generate data flow diagrams and infrastructure dependencies associated with the MoM system.
 4. The system of claim 1, wherein the at least one deviation is indicative of an operational risk or a cyber risk.
 5. The system of claim 4, wherein the at least one deviation is determined based on at least one of the following events: an unexpected change in usage, a new node, an unexpected change in a node, a new relationship between nodes, a threshold of communications exceeded, events occurring at a different time, a higher Service Level Agreement (SLA) requirement, a Recovery Time Objective (RTO) mismatch between nodes, and a Recover Point Objective (RPO) mismatch between nodes.
 6. The system of claim 1, wherein the MoM system is associated with one of a private data center, a private cloud, and public cloud.
 7. The system of claim 1, wherein the policy compute engine is further configured to set permissions to control access to the queue.
 8. The system of claim 1, wherein the policy compute engine is further configured to periodically review critical business functions visualized in the standardized graph.
 9. The system of claim 1, wherein the policy compute engine is further configured to take a mitigating action in response to the alert.
 10. The system of claim 1, wherein the MoM system includes at least one of the following Kafka, IBM MQ, RabbitMQ, TIBCO rendezvous, AWS Kinesis, Microsoft Azure Service Bus, Microsoft Message Queuing (MSMQ), NServiceBus, Simple Queue Service (SQS), Simple Notification Service (SNS), AMPS, and one or more systems for relaying, routing, or transforming messages.
 11. The system of claim 1, wherein the information received from the MOM system by the cloud connector includes a telemetry feed with events of the MoM system, the cloud connector being configured to convert the events into relationships and summarize the relationships.
 12. The system of claim 1, wherein the cloud connector is further configured to: periodically: request a data snapshot of requests from the MoM system; or receive the data snapshot directly from the MoM system through one or more of the following: logs and accounting information; determine status information based on the data snapshot; and convert the status information into the standardized object model.
 13. The system of claim 1, wherein the policy compute engine is further configured to create a report showing relationships between the at least one producer application and the at least one consumer application of the MoM system.
 14. The system of claim 1, wherein the policy is created by the policy compute engine based on one or more producers associated with the at least one producer application and one or more consumers associated with the at least one consumer application.
 15. The system of claim 1, wherein the periodically analyzing the standardized graph is based on one or more of the following: baselined historical data, a set of predetermined permissions, and regulatory requirements.
 16. A method for modeling queue-based message-oriented middleware (MOM), the method comprising: connecting, by a cloud connector, with a MoM system; converting, by the cloud connector, information associated with the MoM system into a standardized object model, wherein the standardized object model includes: a queue-based node associated with the MoM system; at least one producer application, the at least one producer application providing a message to the queue-based node, the message being generated by a producer associated with the at least one producer application; and at least one consumer application, the at least one consumer application receiving the message from the queue-based node, the message being consumed by a consumer associated with the at least one consumer application, the message persisting in a queue until consumed by the at least one consumer application; generating, by a graphical unit, a standardized graph of relationships between the producer and the consumer over a period of time; creating, by a policy compute engine, a policy; periodically analyzing, by the policy compute engine, the standardized graph for at least one deviation from the policy; and in response to detecting the at least one deviation, issuing, by the policy compute engine, an alert.
 17. The method of claim 16, further comprising generating, via the standardized graph, data flow diagrams and infrastructure dependencies associated with the MOM system.
 18. The method of claim 16, further comprising setting, by the policy compute engine, permissions to control access to the queue.
 19. The method of claim 16, further comprising creating, by the policy compute engine, a report showing the relationships between the producer and the consumer of the MoM system.
 20. An enterprise security system for modeling queue-based message-oriented middleware (MoM), the system comprising: a cloud connector configured to: connect with a MoM system; receive information from the MoM system, wherein the information includes at least a telemetry feed with events of the MoM system; compress the received information associated with the MoM system; convert the compressed information associated with the MoM system into a standardized object model; convert, the events into relationships; and summarize the relationships; wherein the standardized object model includes: a queue-based node associated with the MoM system; at least one producer application configured to provide a message to the queue-based node, the message being generated by a producer associated with the at least one producer application; at least one consumer application configured to receive the message from the queue-based node, the message being consumed by a consumer associated with the at least one consumer application, the message persisting in a queue until consumed by the at least one consumer application; wherein the at least one producer application is further configured to receive a confirmation once the at least one consumer application receives the message; a graphical unit configured to generate a standardized graph of relationships between the producer and the consumer over a period of time; and a policy compute engine configured to: create a policy; periodically analyze the standardized graph for at least one deviation from the policy; in response to detecting the at least one deviation, issue an alert; and create a report showing the relationships between the producer and the consumer of the MoM system. 